The mission of the Scientific and Statistical Computing Core is to help NIH researchers with analysis of their functional MRI data. Several levels of help are provided, ranging from short-term immediate aid to long-term development and planning.[unreadable] [unreadable] Consultations:[unreadable] The shortest term help consists of consultations with investigators about specific issues that arise in their research. About 180 such in-person consultations were logged in FY 2007 (including each of the NIH groups listed as NIH Collaborators), and about 750 threads of conversation recorded at our Web-based message board. The issues that arise are quite varied, since there are many steps in carrying out an fMRI data analysis. Common problems that arise include:[unreadable] - How to set up the experimental design so that the data can be analyzed effectively?[unreadable] - Interpretation and correction of MRI imaging artifacts that are visible in the data (the most common of which is caused by patient head motion during scanning).[unreadable] - How to set up the time series analysis to extract the brain activation effects of interest?[unreadable] - How to carry out inter-patient (group) statistical analysis, especially when non-MRI data (e.g., genetic information, age, disease status) needs to be incorporated? (This is perhaps the most common class of question.)[unreadable] [unreadable] Although there are familiar themes in many of these consultations, each meeting raises unique questions and usually requires delving into the goals and details of the research project in order to ensure that nothing crucial is being missed. Complex statistical issues are often brought up, especially in the manuscript preparation phases of the users' projects.[unreadable] [unreadable] Educational Efforts:[unreadable] The Core has developed a 40 hour course on how to design and analyze fMRI data. This course is taught in a week-long "bootcamp", and was taught twice during FY 2007 in the CIT computer classroom. All material for this continually evolving course (sample data, scripts, and PowerPoint/PDF slides) are available on our Web site http://afni.nimh.nih.gov. The course material was extensively revised in FY 2007 to reflect new analysis tools in the AFNI suite, and to incorporate a newer set of sample data that can be used to illustrate the entire process, starting with images output by MRI scanners and continuing through to group analysis.[unreadable] [unreadable] Algorithm and Software Development:[unreadable] The longest term support consists of developing new methods and software for fMRI data analysis. Almost all of our software is incorporated into the AFNI package, which is Unix-based open-source and is available for download by anyone. New programs are created, and old programs modified, in response to specific user requests and in response to the Core's vision of what will be needed in the future. AFNI is "pushed" to NIH computers whenever updates are made; non-NIH systems must manually download the software. Specific developments during FY 2007 include:[unreadable] - Several new features were added to the main time series analysis program, to allow the estimation of individual event modulations in multi-event fMRI experiments, and to provide better diagnostics for incorrect setups of the regression basis time series;[unreadable] - A new multi-modal 2D and 3D image registration program was implemented, which uses several different cost functions and optimization strategies found in the literature;[unreadable] - A "super script" program was written to manage the entire analysis stream of computations;[unreadable] - A technique for controlled spatial smoothing of fMRI time series was created and implemented (most fMRI analysis software tools just add smoothness to the images in an uncontrolled fashion, but this software lets the user specify the level of smoothness desired) -- this technique works in 3D volumes and on 2D models of the folded cortical surface, and corrects some errors in the literature about smoothing on such folded models;[unreadable] - Created nonlinear analysis model software for analyzing MRI time series for two different pharmaceutical applications: alcohol administration (for NIAAA), and Gd-DTPA kinetics in brain tumors (for CC/Radiology);[unreadable] - Developed a new tool for effective functional connectivity analysis of fMRI data -- this new program is faster and more complete than similar codes now being used in this field;[unreadable] - Developed algorithms and software for analysis of manganese (Mn) enhanced MR images (a new technique used for tracing inter-regional connections in animal brains);[unreadable] - Extended our realtime fMRI collaboration with the FMRIF Core and the NIDA Neuroimaging Research Branch, to allow feedback from the imaging data to the patient in the scanner.[unreadable] - Created an script-able cortical surface visualization module, to complement the previous script-able 3D image visualization software in the AFNI GUI.[unreadable] [unreadable] In addition, many small changes were made to the software in response to specific NIH researcher requests and needs. And (of course) many small bug fixes were made -- we pride ourselves on fixing bugs in AFNI within a few days of their report.[unreadable] [unreadable] Our future development plans include an emphasis on fMRI-to-structural image registration (to address deficiencies in existing algorithms), extensions to our methods for calculating brain functional connectivity, adding more brain atlas information to AFNI (including non-human atlases as they become available for inclusion in open-source efforts), improving the inter-software communication capabilities now in AFNI to allow other packages to "talk" to our codes, and leveraging other advanced open-source statistical software (e.g., R) to add their capabilities to AFNI.[unreadable] [unreadable] Extramural Collaborations, etc.:[unreadable] - In FY 2007, we improved our program for nonlinear positive-definite robust diffusion tensor image (DTI) estimation -- this software is being used as the basis for Dr LR Frank's (UCSD) more complicated high angular resolution diffusion imaging (HARDI) software, which will be incorporated into AFNI when ready;[unreadable] - We incorporated into AFNI the brain atlas databases developed by Dr K Zilles (Julich), allowing AFNI users to click on a brain activation map and get a list of the probable structures involved;[unreadable] - We worked with Dr SM Laconte (Baylor) to incorporate his brain-state classifier software into the AFNI framework;[unreadable] - We continued development and support of software for NIfTI (Neuroimaging Informatics Technology Initiative), an NIH-sponsored working group that has defined a simple standard for storing 2D-4D image data for functional brain mapping applications -- this effort also included definition of a new XML-based format for exchanging cortical surface models and development of open-source software for reading and writing this format;[unreadable] - Approximately half of all AFNI message board traffic is from extramural AFNI users. On a space-available basis, the SSCC also allows a few non-NIH users to sit in on the bi-annual AFNI bootcamps at the NIH.[unreadable] - In FY 2007, two extramural institutions (Princeton University and Hanyang University in Seoul) sponsored travel by SSCC members to teach AFNI usage. Dr Cox was also invited to give presentations on the foundations of FMRI data analysis at two conferences.[unreadable] [unreadable] As a measure of our impact, in CY 2006 the principal AFNI paper was cited in 167 publications: 23 from the NIH and 144 from extramural institutions -- the top non-NIH institutions are UC San Diego, the Medical College of Wisconsin, the University of Wisconsin, Massachusetts General Hospital/Harvard University, Stanford University, UC Davis, and the University of Pittsburgh. Through early September, the corresponding figures for CY 2007 are 108 publications: 13 from the NIH and 95 from extramural sites. (Figures from Science Citation Index online.)